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Conference

Biomedical Engineering International Conference 

About: Biomedical Engineering International Conference is an academic conference. The conference publishes majorly in the area(s): Image segmentation & Self-healing hydrogels. Over the lifetime, 720 publication(s) have been published by the conference receiving 2139 citation(s).

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
01 Oct 2013
TL;DR: Glaucoma is classified by extracting two features using retinal fundus images by using Cup to Disc Ratio (CDR) and Ratio of Neuroretinal Rim in inferior, superior, temporal and nasal quadrants to check whether it obeys or violates the ISNT rule.
Abstract: This paper proposes image processing technique for the early detection of glaucoma. Glaucoma is one of the major causes which cause blindness but it was hard to diagnose it in early stages. In this paper glaucoma is classified by extracting two features using retinal fundus images. (i) Cup to Disc Ratio (CDR). (ii) Ratio of Neuroretinal Rim in inferior, superior, temporal and nasal quadrants i.e. (ISNT quadrants) to check whether it obeys or violates the ISNT rule. The novel technique is implemented on 50 retinal images and an accuracy of 94% is achieved taking an average computational time of 1.42 seconds.

44 citations

Proceedings ArticleDOI
22 Mar 2012
TL;DR: The aim of this work is to develop an automatic system with the ability of providing the first assessment to burn injury from burn color images by identifying degree of the burn through segmentation and degree of burn identification.
Abstract: When burn injury occurs, the most important step is to provide treatment to the injury immediately by identifying degree of the burn which can only be diagnosed by specialists. However, specialists for burn trauma are still inadequate for some local hospitals. Hence, the invention of an automatic system that is able to help evaluating the burn would be extremely beneficial to those hospitals. The aim of this work is to develop an automatic system with the ability of providing the first assessment to burn injury from burn color images. The method used in this work can be divided into 2 parts, i.e., burn image segmentation and degree of burn identification. Burn image segmentation employs the Cr-transformation, Luv-transformation and fuzzy c-means clustering technique to separate the burn wound area from healthy skin and then mathematical morphology is applied to reduce segmentation errors. The segmentation algorithm performance is evaluated by the positive predictive value (PPV) and the sensitivity (S). Burn degree identification uses h-transformation and texture analysis to extract feature vectors and the support vector machine (SVM) is applied to identify the degree of burn. The classification results are compared with that of Bayes and K-nearest neighbor classifiers. The experimental results show that our proposed segmentation algorithm yields good results for the burn color images. The PPV and S are about 0.92 and 0.84, respectively. Degree of burn identification experiments show that SVM yields the best results of 89.29 % correct classification on the validation sets of the 4-fold cross validation. SVM also yields 75.33 % correct classification on the blind test experiment.

38 citations

Proceedings ArticleDOI
01 Dec 2012
Abstract: A fall monitor system is necessary to reduce the rate of fall fatalities in elderly people. As an accelerometer has been smaller and inexpensive, it has been becoming widely used in motion detection fields. This paper proposes the falling detection algorithm based on back propagation neural network to detect the fall of elderly people. In the experiment, a tri-axial accelerometer was attached to waists of five healthy and young people. In order to evaluate the performance of the fall detection, five young people were asked to simulate four daily-life activities and four falls; walking, jumping, flopping on bed, rising from bed, front fall, back fall, left fall and right fall. The experimental results show that the proposed algorithm can potentially distinguish the falling activities from the other daily-life activities.

30 citations

Proceedings ArticleDOI
01 Dec 2016
TL;DR: This paper automatically detect as well as to classify the severity of diabetic retinopathy by applying artificial neural network (ANN) and found that the system can give the classification accuracy of 96% and it supports a great help to ophthalmologists.
Abstract: Diabetes retinopathy is a retinal disease that is affected by diabetes on the eyes. The main risk of the disease can lead to blindness. Detection the disease at early stage can rescue the patients from loss of vision. The major purpose of this paper is to automatically detect as well as to classify the severity of diabetic retinopathy. At first, the lesions on the retina especially blood vessels, exudates and microaneurysms are extracted. Features such as area, perimeter and count from these lesions are used to classify the stages of the disease by applying artificial neural network (ANN). We used 214 fundus images from DIARECTDB1 and local databases. We found that the system can give the classification accuracy of 96% and it supports a great help to ophthalmologists.

30 citations

Proceedings ArticleDOI
01 Nov 2014
TL;DR: A step of image segmentation to be divided the optical coherence tomography to find the retinal pigment epithelium (RPE) layer and to detect a shape of drusen in RPE layer and a binary classification to classify two diseases characteristic between AMD and DME is proposed.
Abstract: Age-related macular degeneration (AMD) and Diabetic macular edema (DME) are to lead causes to make a visual loss in people. People are suffered from the use of many time to diagnose and to wait for treatment both of diseases. This paper proposes a step of image segmentation to be divided the optical coherence tomography (OCT) to find the retinal pigment epithelium (RPE) layer and to detect a shape of drusen in RPE layer. Then, the RPE layer is used for finding retinal nerve fiber layer (RNFL) and for detecting a bubble of blood area in RNFL complex. Finally, this method uses a binary classification to classify two diseases characteristic between AMD and DME. We use 16 OCT images of a case study to segmentation and classify two diseases. In the experimental results, 10 images of AMD and 6 images of DME can be detected and classified to accuracy of 87.5%.

30 citations

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Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
201969
201877
201772
201668
201577
201487